As we move through March 2025, two transformative AI trends have already reshaped the technological landscape this year: Deep Research (February 2025) and MCP (November 2024).

I’m documenting these observations primarily for my own reference. It’s fascinating how groundbreaking innovations often emerge without immediate recognition of their full significance. From my perspective, MCP represents a fundamental shift in AI capabilities, while Deep Research, though slightly less revolutionary, still marks a significant advancement in how AI systems approach complex problems. I’m curious to see how these technologies evolve in the coming months.

Deep Research

While the AI landscape of 2023-2024 saw early experiments with autonomous AI systems like AutoGPT, Deep Research represents the first truly practical implementation of autonomous decision-making in 2025. Unlike its predecessors, Deep Research isn’t just theoretically interesting—it delivers consistently valuable results in real-world applications.

What sets Deep Research apart is how it harnesses the power of multiple, sequential LLM calls without relying on rigid, predefined workflows. Instead, the system dynamically determines its next steps and actions based on intermediate results. This adaptive approach substantially enhances its capacity to tackle intricate, multi-faceted problems while maintaining reliability—something earlier autonomous systems struggled to achieve.

This non-workflow-based multi-call architecture requires exceptionally capable foundation models. Any reasoning or planning error can cascade through the entire process, potentially derailing the solution. The successful implementation of Deep Research indirectly highlights the remarkable progress in underlying model capabilities, as demonstrated by the latest Claude 3.5 and 3.7 models.

Below is a simplified representation of Deep Research’s core logic:

// Deep Research Core Logic - Multi-LLM collaboration in research

FUNCTION DeepResearch(query, max_depth = 3):
  // Initialize research state: query, findings, sources, subtopics, depth
  
  // First LLM Call: Plan the research approach
  researchPlan = planResearch(query)
  
  WHILE current_depth < max_depth:
    // Second LLM Call: Decide next best action
    nextAction = determineNextAction(["search", "analyze", "expand", "synthesize"])
    
    // Third LLM Call: Execute chosen action
    IF nextAction == "search":
      results = performWebSearch(generateSearchQuery())
    ELSE IF nextAction == "analyze":
      results = analyzeExistingFindings()
    ELSE IF nextAction == "expand":
      results = identifyNewSubtopics()
    ELSE:
      results = synthesizeCurrentFindings()
    
    // Fourth LLM Call: Evaluate and decide to continue or stop
    shouldContinue = evaluateProgress(results)
    IF NOT shouldContinue:
      BREAK
    
    current_depth += 1
  
  // Final LLM Call: Generate comprehensive report
  RETURN generateReport()

MCP

In late 2024, Anthropic introduced the MCP (Model Context Protocol), representing a critical advancement in how LLMs interact with external applications. While function calling capabilities have existed for some time, MCP’s revolutionary contribution is establishing a unified, standardized protocol for these interactions.

The true significance of MCP lies not in enabling tool use (which was already possible), but in creating a common language and framework that developers across the ecosystem can implement. This standardization means that once an LLM understands MCP, it can potentially interact with any MCP-compatible application without additional training or custom integration work.

Since MCP’s introduction, we’ve witnessed exponential growth in compatible applications, from sophisticated design software like Blender to specialized scientific tools and everyday productivity applications. This interoperability dramatically reduces the integration burden for developers while expanding the range of practical challenges AI systems can address through a single, consistent interface.

Here’s a concise overview of the MCP architecture:

// Model Context Protocol (MCP) - Client/Server Architecture

// Component 1: MCP Server (Data/Tool Provider)
FUNCTION MCPServer:
  // Register capabilities
  FUNCTION initialize():
    Register available resources (data sources)
    Register available tools (functions)
    Configure access permissions
  
  // Handle client requests
  FUNCTION handleRequest(request):
    IF request.type == "RESOURCE":
      Retrieve and return requested data
    ELSE IF request.type == "TOOL":
      Validate parameters
      Execute requested tool function
      Return results
    ELSE IF request.type == "CAPABILITY":
      Return list of available resources and tools

// Component 2: MCP Client (AI-powered Application)
FUNCTION MCPClient:
  // Connect to server
  FUNCTION connectToServer(serverUri):
    Establish transport connection (HTTP/SSE or stdio)
    Fetch server capabilities
    
  // Request data from server
  FUNCTION fetchResource(resourceId, parameters):
    Send resource request to server
    Process and return response
    
  // Execute tool on server
  FUNCTION executeTool(toolName, parameters):
    Validate parameters locally
    Send tool execution request to server
    Return results to LLM
    
  // Process user request with LLM
  FUNCTION processRequest(userPrompt):
    LLM analyzes request and context
    
    // Dynamically determine needed resources/tools
    resource = LLM.identifyNeededResource()
    IF resource:
      data = fetchResource(resource.id, resource.parameters)
      LLM.addContext(data)
      
    tool = LLM.identifyNeededTool()
    IF tool:
      result = executeTool(tool.name, tool.parameters)
      LLM.processResult(result)
      
    RETURN LLM.generateResponse()